Prediction of newborn sex with neural networks approach to fetal cardiotocograms classification

Cardiotocographic monitoring (CTG) is the primary biophysical method for assessment of the fetal state. It consists in analysis of fetal heart rate variability, uterine contraction activity and fetal movements signal. Visual analysis of printed cardiotocographic traces is difficult so the computerized fetal monitoring systems are a standard in clinical centres. In the proposed work we investigated the ability of the application of artificial neural networks for the prediction of newborn sex using parameters of quantitative description of CTG traces. We examined the influence of input data representation (numerical or categorical) and the influence of the gestational age on the classification quality. We obtained the classification quality at a level of 80% and therefore we may state, that there is rather a strong relation between the fetal gender and the fetal heart rate variability.